Hack Five For members

The Fifth Elephant Open Source AI Hackathon 2024

GenAI makers and creators contest and showcase

Make a submission

Accepting submissions till 15 Feb 2024, 11:00 PM

Hasura, Bangalore




The Fifth Elephant Open Source AI Hackathon started on 5 January 2024 and reached its finale with a Demo Day event on 12 April 2024, when the winners of the two month long contest were chosen.

The aim of this hackathon was to encourage individuals/teams to apply and incubate innovative AI ideas/use cases and publish them as open source projects.

  • The hackathon contest participants worked with mentors for over two months to refine their ideas, and advance them to a stage where they are viable projects that could be pursued beyond the hackathon.
  • the project teams worked on AI’s application in education, accessibility, creative expression, scientific research, languages, under the overall theme of AI for India.
  • competing projects were judged on impact and relevance, innovation and creativity, technical soundness and code quality, scope of expansion, reusability and ease of adoption.

📋 Read Hack5 V1 Report

As a campaign to raise awareness and drive up developer adoption of AI and open source technologies, the hackathon was a great success. It helped shine light on the agility that open source technology enables for creative and innovative developers.

Open Source AI Hackathon Winners


“...each one of the contestants put in tremendous effort. And we saw the passion in every person, trying to do things not for winning, but about really building your projects. After a long time, I am attending such a hackathon where young folks are so passionate about building. Kudos to all of you”.
- Rukma Talwadker, Jury Member, Senior Principal Scientist at Games 24x7

“I really enjoyed judging all the projects - lot of interesting work. The Fifth Elephant has done a great job with mentoring and curating this hackathon”.
- Tanuja Ganu, Jury Member, Principal RSDE Manager, Microsoft India

“The hallmark of this hackathon was getting younger people to code for a longer period of time as opposed to a typical hackathon which turns out to be about — how do you build the coolest thing in the shortest period of time”.
- Sumod Mohan, mentor.

“What is impressive about this particular hackathon is, it is not just about cool ideas and fancy demos. It is actually about building a product or a software or a model that can live beyond the demo (and contest).”
- Soma Dhavala, team member at Project Seshu

“It was only through putting my ideas to code that I learnt what the specificity of implementing these (LLMs) were. I began my journey with a sense of hope and commitment towards FOSS principles, and the Hackathon only reinforced my belief that collaboration maketh a better product.”
- Sankalp Srivastava, Creator of Project Schematise

Key highlights from the hackathon

During the course of 12 weeks, the hackathon involved:

  1. Started off on 5 January 2024 and invited open source ideas and projects.
  2. Mentorship sessions in February for all project teams. Mentors included Abhishek H Mishra aka Tokenbender, Arvind Saraf, Bharat Shetty, Ramesh Hariharan, Sidharth Ramachandran, Simrat Hanspal, Sumod Mohan and Vinayak Hegde.
  3. The 10 best from 40 applications were chosen for the Demo Showcase.
  4. An involved peer-review process helped further refine projects between March 1st - 15th, followed by extensive rehearsals from April 8th - 10th, 2024.
  5. On Demo Showcase Day - we had project demos from 10 qualifying teams; 5 project winners were chosen on 12 April 2024.

The Prizes

🏆 Five prizes of ₹1,00,000 (One lakh rupees) per theme, were awarded to winning projects.
The prizes for this hackathon have been sponsored by Meta.

Note: Apart from the contest prizes, Microsoft has offered internships to the contestants.


  1. Ashok Hariharan heads data and business intelligence at United Nations Volunteers.
  2. Rukma Talwadker is Senior principal scientist at Games24x7.
  3. Shubha Shedthikere is a Senior Manager in the Data Science team at Swiggy.
  4. Sunil Abraham is the Public Policy Director for Data Economy and Emerging Tech at Meta, India.
  5. Tanuja Ganu is a Principal RSDE Manager at Microsoft Research India.


  1. Abhishek Mishra is a is creator of CodeCherryPop LLM series.
  2. Arvind Saraf is a computer scientist, engineering leader, entrepreneur trained at IIT, MIT and Google.
  3. Simrat Hanspal is currently spearheading AI product strategy at Hasura.
  4. Sumod Mohan is the co-founder and CEO of AutoInfer.


About The Fifth Elephant

The Fifth Elephant is a community of practitioners, who share feedback on data, AI and ML practices in the industry. If you like the work that The Fifth Elephant does and want to support its activities - review of Papers, Books, building the innovation ecosystem in India through hackathons and conferences - contribute by picking up a membership.


💬 Post a comment with your questions here, or join The Fifth Elephant Telegram group and the WhatsApp group.

Follow @fifthel on Twitter.

📞 For any inquiries, call The Fifth Elephant at +91-7676332020.

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Hosted by

The Fifth Elephant hackathons

Supported by


All about data science and machine learning

Venue host

Welcome to the events page for events hosted at The Terrace @ Hasura. more


Providing all founders, at any stage, with free resources to build a successful startup.

darsh agarwal


EasyEats: Tailored Ready-to-Eat Meal Suggestions

Submitted Feb 13, 2024


Due to fast-paced and long work environments, people are struggling to pay due to attention to their meals.
Most of these meals are ready-to-eat either delivered by restaurants or packaged meals from convinience stores.
Due to the purpose of these meals they cannot be a substitute for a balance diet; Especially if they are consumed without dietary planning.
This can be contributed to high amounts of calories, fat, sugar, and sodium and lack of low in nutrients, such as fiber, vitamins, and minerals.

Our Solution

Developing a service that integrates with E-Carts of food order, convenience store platforms and provide nutritional insights, statistics and recommendations, to help consumers make healthier choices, and enhance the user experience in this trillion dollar market.


Datasets: Limited item options from particular convenience stores or restaurants limit the datasets.
Bypassing Variance: Reliance on user participation can be bypassed using content based filtering.
Parameter Tuning: Weighing the priority of a general healthy diet with user specialized data in forming recommendations.
Considerations for palette and budget
Time Series Component: Consideration of all meals consumed by the user on a daily, weekly, or monthly scale to form recommendations.

Outcomes/ impact 🥅

  • The USP of the product is it aims for a more structured and healthier consumption of ready-to-eat, order-to-eat meals for regular consumers to promote a healthier lifestyle.
  • Therefore the outcome of the project is hopeful towards developing a model that can recommend items based on user utility in this domain.

Current status/progress with respect to the roadmap 🚅

  1. Dataset generation: An intermediate method of using language models to judge the nutrition content and calorific count for each dish based on its description. Data is created for 1 zipcode in Dallas and 1 in Austin, texas. attributes included are:
    • nutrient contents
    • estimated calories
    • possible allergens
  2. User calories: Based on the user’s height,weight,age,sex and activeness an in-house developed service (YouCal)[https://github.com/FoodStats/YouCal] is being utillised; which is trained in reference to the Mayo clinic researched parameters!
  3. User utility generation: formulated as follows meals
    • Explict utility
      • Nutritional content (Calories + Nutrients)
      • Price
      • User ratings
    • Implicit utility
      • user flavour profile
      • user vested interest (time spent on an item)
  4. Utility function:
    • An IU-SLSQP (Implicit utility with Sequential Least square optimizer for non linear programming) for user preferences
    • Explicit utility
      • calculated for Nutrition based on user goals
      • price
      • Customer ratings
    • combined over a Multi Atribute Utility Theory function (MAUTF).
    • IU-GA (Genetic algorithm also explored)
  5. Evaluation: base results run, with acceptible outcomes.
  6. GUI Achieved.
  7. Backend API developed.

Future Scope ⏫

  1. Refining app/web interface.
  2. API destribution.
  3. using the said interface for crawlers to collect implicit utility data, ie. frequency of attribute,browse time.
  4. domain expertise oriented tokenisation of estimation of nutrition content for language model being used. Lot of scope in the fine tuning of LLM based data generation.

An approach would be to leverage a USDA datasets for individual ingredients to leverage an NLP encoder such as BERT to tokenise nutritional content based of dish discriptions/ recipes.

  1. Suggestion on the model itself. Domain experts based utility functions finetuning.
  2. Introducing a long term time series component
  3. Further testing and implementation of results with real users.

For more details head over to



Base dataset



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Make a submission

Accepting submissions till 15 Feb 2024, 11:00 PM

Hasura, Bangalore

Hosted by

The Fifth Elephant hackathons

Supported by


All about data science and machine learning

Venue host

Welcome to the events page for events hosted at The Terrace @ Hasura. more


Providing all founders, at any stage, with free resources to build a successful startup.